class_docstr: Creates random scatter plots as a testing utility.
  
  Data generation uses scikit-learn make_blobs to create scatter plots that
  have reasonable visual features and multiple colors.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  n_samples : int, default: 100
      The number of points to generate for the scatter plot
  
  n_blobs : int or array of shape [n_centers, 2]
      Define the number of blobs to create or specify their centers.
  
  random_state : int, RandomState or None:
      Used to specify the seed of the random state to ensure tests work.
code: def __init__(
      self,
      ax=None,
      n_samples=100,
      n_blobs=3,
      random_state=None,
      **kwargs,
  ):
      super(RandomVisualizer, self).__init__(ax=ax, **kwargs)
      if isinstance(random_state, (int, float)) or random_state is None:
          random_state = np.random.RandomState(random_state)
  
      self.n_samples = n_samples
      self.n_blobs = n_blobs
      self.random_state = random_state
  
file_path: yellowbrick/tests/rand.py
imports: import numpy
  from yellowbrick.base import Visualizer
  from yellowbrick.style import resolve_colors
  from sklearn.datasets import make_blobs
label_desc: Use image node_id 1 to create a new RandomVisualizer object from inherited base classes: Visualizer with example: obj = RandomVisualizer(ax, n_samples, n_blobs, random_state)